Specificity, Sensitivity and AUC
Rapido
New Altair Community Member
Hello,
I'm doing some research for my bachelor thesis and have some questions about Rapidminer 5 and AUC.
Im trying to compare three Classificators ( SVM, Linear Regression and Linear Discriminant Analysis) with 10 fold crossvalidation and AUC as MAIN CRITERION.
For Linear Regression and Linear Discriminant Analysis I always get the same AUC (0.5). No matter how many variables I'm using or how I normalize the data before. On the other hand Specificity and Sensitivity values change. For example, the Linear Regression Model delivers a Specificity 78%, Sensitivity 71% and AUC 0.5. Linear Discriminant Analysis delivers a Specificity 70.81%, Sensitivity 64.5% and AUC 0.5
The SVM values (Specificity 81.5%, Sensitivity 79.5%, AUC 0,904) seems to be OK.
Can this be correct or is this a data Problem? Does a Linear Model always have an AUC of 0.5 no matter if Specificity and Sensitivity values are both high ?
I couldn't find any literature infos about this strange behavior. I always thought that with a high Specificity eg. 85% and Sensitivity e.g. 87% I get a high AUC value.
The whole process with Training Data can be downloaded here http://www.myexperiment.org/packs/151/download
I'm doing some research for my bachelor thesis and have some questions about Rapidminer 5 and AUC.
Im trying to compare three Classificators ( SVM, Linear Regression and Linear Discriminant Analysis) with 10 fold crossvalidation and AUC as MAIN CRITERION.
For Linear Regression and Linear Discriminant Analysis I always get the same AUC (0.5). No matter how many variables I'm using or how I normalize the data before. On the other hand Specificity and Sensitivity values change. For example, the Linear Regression Model delivers a Specificity 78%, Sensitivity 71% and AUC 0.5. Linear Discriminant Analysis delivers a Specificity 70.81%, Sensitivity 64.5% and AUC 0.5
The SVM values (Specificity 81.5%, Sensitivity 79.5%, AUC 0,904) seems to be OK.
Can this be correct or is this a data Problem? Does a Linear Model always have an AUC of 0.5 no matter if Specificity and Sensitivity values are both high ?
I couldn't find any literature infos about this strange behavior. I always thought that with a high Specificity eg. 85% and Sensitivity e.g. 87% I get a high AUC value.
The whole process with Training Data can be downloaded here http://www.myexperiment.org/packs/151/download
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-1
Answers
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Hi there Rapido,
I couldn't help noticing that when you normalise you have not selected the 'create view' option, which means that each normalisation changes the underlying data. By the time the LR XVal starts the data has in fact been normalised twice! You can use the double-headed blue arrow in the Process view to see the operator sequence.
This is quite a common novice gotcha, and Sebastian ( who actually knows what he is talking about ), runs through the explanation here.
http://rapid-i.com/rapidforum/index.php/topic,2588.0.html
Hopefully that will clear some of the fog!
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I also got this problem. I run the data by Linear Discrimination Analysis Model with many attribute sets. But the results of these sets were found the distinction of sensitivities, specificities, and accuracies but except AUC values were always appeared 1 (AUC optimistic always appeared 0).
Do I correct? I tried to find the literature to explain for this answer, but I cannot find. Why?0 -
FYI: The URL mentioned above throws a "Page Not Found" error. http://rapid-i.com/rapidforum/index.php/topic,2588.0.html
0
Altair Employee